Spectroscopy and Spectral Analysis, Volume. 42, Issue 7, 2135(2022)
Experimental Research on Coal-Rock Identification Method Based on
Figures & Tables(15)
Fig. 3. Experimental chart of ash content of coal and rock samples
Fig. 4. Set up for determining the volatile matter in coal and gangue
Fig. 5. Model structure of PCA-BP neural network based on visible-near infrared spectra
Fig. 7. Structure of PCA-BP neural network based on sample components
Fig. 8. Recognition rate of PCA-BP neural network model based on sample components
Table 1. Test results of industrial indicators of coal samples from two mines
View tableView in ArticleTable 1. Test results of industrial indicators of coal samples from two mines
样本编号 Mad/% Vad/% Aad/% XQM1 1.60 28.16 33.74 XQM2 1.71 27.16 29.60 XQM3 1.00 28.37 33.66 XQM4 1.50 28.07 27.08 XQM5 1.10 27.63 31.38 XQM6 1.20 28.57 31.96 XQM7 1.20 27.50 31.51 XQG1 1.40 10.84 85.05 XQG2 1.00 10.66 86.11 XQG3 1.40 12.39 86.31 XQG4 1.20 10.83 86.44 P2M1 0.99 23.67 40.80 P2M2 0.85 33.15 5.86 P2M3 0.79 32.09 6.61 P2M4 1.06 33.74 7.05 P2M5 0.92 34.25 11.24 P2M6 1.32 26.85 22.71 P2M7 0.90 29.11 17.00 P2M8 1.38 25.59 32.60 P2G1 0.40 2.71 97.00 P2G2 0.80 9.26 87.10 P2G3 1.80 9.64 88.46 P2G4 0.70 7.27 85.43 P2G5 1.70 8.96 89.78 P2G6 2.10 12.31 82.57 P2G7 0.80 10.75 84.12 P2G8 0.70 10.97 88.08 P2G9 1.03 6.93 92.19 P2G10 1.11 9.20 88.31
Table 2. Oxygen percentage contents of coal and rock samples from two mines
View tableView in ArticleTable 2. Oxygen percentage contents of coal and rock samples from two mines
样品编号 Na2O MgO Al2O3 SiO2 P2O5 SO3 K2O CaO TiO2 Fe2O3 XQM1 0.37 0.51 30.00 57.10 0.24 2.37 0.00 1.93 1.53 5.97 XQM2 0.34 0.53 27.93 57.74 0.16 4.29 1.13 0.19 2.08 5.62 XQM3 0.22 0.52 28.71 58.42 0.21 2.97 0.13 1.19 1.86 5.76 XQM4 0.36 0.55 27.16 57.34 0.18 4.66 1.16 0.67 2.11 5.82 XQM5 0.33 0.52 28.68 57.92 0.16 3.95 0.82 0.09 1.97 5.56 XQM6 0.39 0.51 28.66 57.08 0.17 3.36 0.50 1.66 1.93 5.76 XQM7 0.30 0.51 28.37 57.88 0.17 3.77 1.05 0.50 1.98 5.48 XQG1 0.17 0.33 33.15 60.68 0.10 0.16 0.44 0.00 1.44 3.53 XQG2 0.36 0.31 32.37 61.22 0.09 0.17 0.87 0.00 1.20 3.42 XQG3 0.44 0.31 35.67 58.63 0.10 0.09 0.16 0.00 1.08 3.53 XQG4 0.28 0.32 32.58 60.48 0.10 0.16 1.08 0.00 1.46 3.55 P2M1 0.33 0.43 28.04 60.24 0.18 2.09 0.97 1.41 1.31 4.99 P2M2 1.49 0.90 13.64 47.82 5.63 7.47 0.56 11.73 1.80 8.97 P2M3 1.53 0.99 17.76 57.14 1.15 9.36 0.00 0.66 2.80 8.60 P2M4 1.24 0.99 15.99 55.32 1.70 10.35 0.00 1.78 3.04 9.58 P2M5 0.65 2.57 9.71 38.28 0.23 5.71 0.00 33.71 0.85 8.30 P2M6 0.66 0.56 23.46 52.59 0.17 8.65 2.24 3.65 1.86 6.17 P2M7 0.90 0.62 24.23 51.89 0.19 6.48 0.98 6.71 1.91 6.10 P2M8 0.33 0.52 27.18 56.61 0.26 3.53 1.06 3.00 1.56 5.97 P2G1 0.05 0.32 19.99 68.38 0.10 0.13 6.01 0.77 1.11 3.15 P2G2 0.19 0.31 29.66 61.64 0.10 0.23 3.51 0.00 1.30 3.08 P2G3 0.20 0.31 30.13 62.91 0.10 0.12 1.55 0.00 1.26 3.42 P2G4 0.21 0.31 30.23 62.87 0.10 0.12 1.52 0.00 1.23 3.42 P2G5 0.31 0.30 28.81 62.82 0.09 0.24 2.78 0.07 1.25 3.34 P2G6 0.51 0.31 34.54 58.91 0.10 0.15 0.73 0.00 1.51 3.26 P2G7 0.36 0.30 29.80 61.57 0.09 0.54 2.91 0.00 1.26 3.18 P2G8 0.13 0.30 27.82 63.56 0.10 0.18 1.61 0.56 1.06 4.69 P2G9 0.37 0.29 29.40 61.34 0.09 0.10 4.28 0.00 1.00 3.15 P2G10 0.28 0.29 28.29 63.86 0.09 0.40 2.34 0.01 0.92 3.52
Table 3. Model accuracy of PCA-SVM model based on visible-near infrared spectra
View tableView in ArticleTable 3. Model accuracy of PCA-SVM model based on visible-near infrared spectra
组别 最优惩罚
参数c方差参数
g建模精度
/%验证精度
/%1 0.378 93 6.964 4 85 44.44 2 0.5 1.148 7 75 75 3 1.071 8 36.758 3 100 66.67 4 1 2.143 5 75 55.56 5 0.000 976 56 0.000 976 56 65 22.22 6 1.624 5 0.378 93 75 55.56 7 13.928 8 4 90 33.33 8 0.933 03 12.125 7 90 22.22 9 1.231 1 27.857 6 95 66.67 10 19.698 3 1.741 1 95 66.67 11 675.588 1 0.435 28 95 66.67 12 0.000 976 56 0.000 976 56 55 33.33 13 1.148 7 315.173 100 33.33 14 27.857 6 2.639 90 44.44 15 0.000 976 56 0.000 976 56 55 44.44 16 0.000 976 56 36.758 3 85 44.44 17 3.249 0.203 06 65 22.22 18 1.319 5 48.502 9 90 44.44 19 1.231 1 84.448 5 95 44.44 20 73.516 7 6.062 9 100 33.33
Table 4. Accuracy of KPCA-SVM model
View tableView in ArticleTable 4. Accuracy of KPCA-SVM model
组别 最优惩罚
参数c方差参数
g建模精度
/%验证精度
/%1 0.319 5 2.639 100 100 2 0.176 78 0.659 75 90 88.89 3 0.267 94 0.5 90 100 4 0.574 35 0.435 28 100 77.78 5 0.435 28 0.353 55 95 77.78 6 0.574 35 0.757 86 95 88.89 7 0.203 06 0.659 75 95 77.78 8 34.296 8 4 100 100 9 0.233 26 0.757 86 85 100 10 0.000 976 56 2.462 3 100 100 11 2.828 4 1.414 2 100 100 12 0.267 94 3.249 95 88.89 13 0.287 17 1.148 7 100 66.67 14 0.233 26 0.812 25 95 88.89 15 0.25 0.615 57 95 77.78 16 3.482 2 0.574 35 90 100 17 0.307 79 3.031 4 95 88.89 18 548.748 1.866 1 100 100 19 0.5 2.639 95 88.89 20 0.203 06 0.574 35 95 100
Table 5. Accuracy of principal component analysis combined with support vector machine model
View tableView in ArticleTable 5. Accuracy of principal component analysis combined with support vector machine model
组别 最优惩罚
参数c方差参数
g建模精度
/%验证精度
/%1 0.574 35 5.278 75 44.44 2 10.556 1 0.217 64 70 66.67 3 0.757 86 111.430 5 90 44.44 4 6.062 9 6.964 4 85 33.33 5 256 1.515 7 95 22.22 6 13.928 8 0.066 986 80 66.67 7 0.757 86 207.936 6 100 33.33 8 1.231 1 630.345 9 100 22.22 9 137.187 0.094 732 70 66.67 10 0.000 976 56 2.639 70 66.67 11 6.062 9 0.066 986 75 66.67 12 0.267 94 0.870 55 100 100 13 0.757 86 34.296 8 95 33.33 14 6.062 9 2.828 4 90 66.67 15 0.707 11 64 90 44.44 16 0.870 55 55.715 2 85 44.44 17 25.992 1 1.414 2 80 66.67 18 0.615 57 51.984 2 80 44.44 19 0.870 55 2.462 3 75 44.44 20 0.615 57 1.741 1 70 33.33
Table 6. Model accuracy of KPCA-SVM model based on sample components
View tableView in ArticleTable 6. Model accuracy of KPCA-SVM model based on sample components
组别 最优惩罚
参数c方差参数
g建模精度
/%验证精度
/%1 0.203 06 0.659 75 100 88.89 2 0.329 88 0.870 55 100 100 3 0.353 55 0.757 86 100 100 4 0.233 26 0.406 13 100 88.89 5 0.435 28 0.406 13 95 100 6 0.329 88 0.435 28 100 88.89 7 0.287 17 0.535 89 95 100 8 0.757 86 0.329 88 100 88.89 9 24.251 5 0.016 746 100 100 10 0.000 976 56 0.000 976 56 95 100 11 0.000 976 56 0.000 976 56 95 100 12 0.267 94 0.870 55 100 100 13 0.329 88 0.378 93 100 88.89 14 0.707 11 0.535 89 100 100 15 12.125 7 0.033 493 100 100 16 0.000 976 56 0.000 976 56 100 88.89 17 0.267 94 0.435 28 100 88.89 18 0.267 94 0.466 52 95 100 19 0.233 26 0.659 75 95 100 20 0.267 94 1 100 77.78
Table 7. Algorithm model accuracy/recognition rate comparison
View tableView in ArticleTable 7. Algorithm model accuracy/recognition rate comparison
特征提取 算法模型 建模精度
/%验证精度/
识别率/%可见光-
近红外光谱PCA-SVM 83.75 66.67 PCA-BP / 46.11 KPCA-SVM 95.5 90.56 样本成分含量 PCA-SVM 83.75 50.56 PCA-BP / 46.11 KPCA-SVM 98.5 95
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Liang-ji XU, Xue-ying MENG, Ren WEI, Kun ZHANG. Experimental Research on Coal-Rock Identification Method Based on[J]. Spectroscopy and Spectral Analysis, 2022, 42(7): 2135
Paper Information
Category: Orginal Article
Received: Jan. 11, 2021
Accepted: --
Published Online: Nov. 16, 2022
The Author Email: XU Liang-ji (ljxu@aust.edu.cn)
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